| 1 |
Learning to rank networked entities. Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal. KDD 2006, 14-23. Cited by 35Web SearchBibTeXDownload |
| 2 |
Learning random walks to rank nodes in graphs. Alekh Agarwal, Soumen Chakrabarti. ICML 2007, 9-16. Cited by 16Web SearchBibTeXDownload |
| 3 |
Learning Parameters in Entity Relationship Graphs from Ranking Preferences. Soumen Chakrabarti, Alekh Agarwal. PKDD 2006, 91-102. Cited by 7Web SearchBibTeXDownload |
| 4 |
Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. Alekh Agarwal, Ofer Dekel, Lin Xiao. COLT 2010, 28-40. Web SearchBibTeXDownload |
| 5 |
Stochastic convex optimization with bandit feedback. Alekh Agarwal, Dean P. Foster, Daniel Hsu, Sham M. Kakade, Alexander Rakhlin. CoRR (abs/1107.1744) (2011). Web SearchBibTeXDownload |
| 6 |
The Generalization Ability of Online Algorithms for Dependent Data. Alekh Agarwal, John Duchi. CoRR (abs/1110.2529) (2011). Web SearchBibTeXDownload |
| 7 |
A Reliable Effective Terascale Linear Learning System. Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford. CoRR (abs/1110.4198) (2011). Web SearchBibTeXDownload |
| 8 |
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. Alekh Agarwal, Sahand Negahban, Martin J. Wainwright. CoRR (abs/1102.4807) (2011). Web SearchBibTeXDownload |
| 9 |
Fast global convergence of gradient methods for high-dimensional statistical recovery. Alekh Agarwal, Sahand Negahban, Martin J. Wainwright. CoRR (abs/1104.4824) (2011). Web SearchBibTeXDownload |
| 10 |
Online and Batch Learning Algorithms for Data with Missing Features. Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett. CoRR (abs/1104.0729) (2011). Web SearchBibTeXDownload |
| 11 |
A Stochastic View of Optimal Regret through Minimax Duality. Jacob Abernethy, Alekh Agarwal, Peter L. Bartlett, Alexander Rakhlin. CoRR (abs/0903.5328) (2009). Web SearchBibTeXDownload |
| 12 |
Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes. Pradeep D. Ravikumar, Alekh Agarwal, Martin J. Wainwright. ICML 2008, 800-807. Web SearchBibTeXDownload |
| 13 |
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright. Journal of Machine Learning Research (11): 1043-1080 (2010). Web SearchBibTeXDownload |
| 14 |
Optimal Allocation Strategies for the Dark Pool Problem. Alekh Agarwal, Peter L. Bartlett, Max Dama. Journal of Machine Learning Research - Proceedings Track (9): 9-16 (2010). Web SearchBibTeXDownload |
| 15 |
Oracle inequalities for computationally budgeted model selection. Alekh Agarwal, John C. Duchi, Peter L. Bartlett, Clement Levrard. Journal of Machine Learning Research - Proceedings Track (19): 69-86 (2011). Web SearchBibTeXDownload |
| 16 |
Information-theoretic lower bounds on the oracle complexity of convex optimization. Alekh Agarwal, Peter L. Bartlett, Pradeep D. Ravikumar, Martin J. Wainwright. NIPS 2009, 1-9. Web SearchBibTeXDownload |
| 17 |
Fast global convergence rates of gradient methods for high-dimensional statistical recovery. Alekh Agarwal, Sahand Negahban, Martin J. Wainwright. NIPS 2010, 37-45. Web SearchBibTeXDownload |
| 18 |
An Analysis of Inference with the Universum. Fabian H. Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf. NIPS 2007. Web SearchBibTeXDownload |
| 19 |
Distributed Dual Averaging In Networks. John Duchi, Alekh Agarwal, Martin J. Wainwright. NIPS 2010, 550-558. Web SearchBibTeXDownload |
| 20 |
Learning with Missing Features. Afshin Rostamizadeh, Alekh Agarwal, Peter L. Bartlett. UAI 2011, 635-642. Web SearchBibTeXDownload |
